An unobtrusive behavioral model of "gross national happiness"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
You Are Not a Gadget: A Manifesto
You Are Not a Gadget: A Manifesto
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Network properties and social sharing of emotions in social awareness streams
Proceedings of the ACM 2011 conference on Computer supported cooperative work
The hidden image of the city: sensing community well-being from urban mobility
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Twitter zombie: architecture for capturing, socially transforming and analyzing the twittersphere
Proceedings of the 17th ACM international conference on Supporting group work
Finger on the pulse: identifying deprivation using transit flow analysis
Proceedings of the 2013 conference on Computer supported cooperative work
What's in Twitter: I Know What Parties are Popular and Who You are Supporting Now!
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Urban: crowdsourcing for the good of London
Proceedings of the 22nd international conference on World Wide Web companion
Identifying purpose behind electoral tweets
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
Computational social science: CSCW in the social media era
Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing
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Policy makers are calling for new socio-economic measures that reflect subjective well-being, to complement traditional measures of material welfare as the Gross Domestic Product (GDP). Self-reporting has been found to be reasonably accurate in measuring one's well-being and conveniently tallies with sentiment expressed on social media (e.g., those satisfied with life use more positive than negative words in their Facebook status updates). Social media content can thus be used to track well-being of individuals. A question left unexplored is whether such content can be used to track well-being of entire physical communities as well. To this end, we consider Twitter users based in a variety of London census communities, and study the relationship between sentiment expressed in tweets and community socio-economic well-being. We find that the two are highly correlated: the higher the normalized sentiment score of a community's tweets, the higher the community's socio-economic well-being. This suggests that monitoring tweets is an effective way of tracking community well-being too.